DataRobot-Research/drdocs huggingface dataset:
---
title: AutoML public preview features
description: Read preliminary documentation for AutoML features currently in the DataRobot public preview pipeline.
section_name: AutoML
maturity: public-preview
---

# AutoML public preview features {: #automl-public-preview-features }

{% include 'includes/pub-preview-notice-include.md' %}

## Available AutoML public preview documentation {: #available-automl-public-preview-documentation }

Public preview for... | Describes...
----- | ------
[Document AI](doc-ai/index) | Learn how to use raw documents as an input to modeling.
[GPU support](gpus) |  Enable GPU support to improve runtime for deep learning models.
[Prediction Explanations for clusters](cluster-pe)  | Generate and interpret Prediction Explanations for clustering projects.
[Detailed view for blueprints](blueprint-toggle) | Toggle between a summary view and a detailed view when working with the Composable ML blueprint editor.
[Quantile regression metric](quantile-reg) | Predict a conditional value (a quantile) for a project.
[Configure hyperparameters for custom tasks](cml-hyperparam) | Define the hyperparameters for a custom task.
---
title: Business accelerators
description: A catalog of UI-based, end-to-end walkthroughs that address common industry-specific problems.
---

# Business accelerators {: #business-accelerators }

This section provides access to a catalog of UI-based, end-to-end walkthroughs, based on best practices and patterns, that address common industry-specific problems.

Use case | Description
-------- | -----------
[Business application briefs](biz-app-briefs) | A variety of quick summary applications with an accompanying No-Code AI App to provide an overview of possible uses.
[Purchase card fraud detection](p-card-detect) | Helps organizations that employ purchase cards for procurement to monitor for fraud and misuse.
[Likelihood of a loan default](loan-default) | Helps minimize risk by predicting the likelihood that a borrower will not repay their loan.
[Late shipment predictions](late-ship) | Helps supply chain managers can evaluate root cause and then implement short-term and long-term adjustments that prevent shipping delays.
[Reduce hospital readmission rates](readmit) | Helps to reduce the 30-Day readmissions rate by predicting at-risk patients.
[Triage insurance claim](insurance-claims) | Helps insurers assess claim complexity and severity as early as possible for optimized routing and  handling.
[Fraudulent claim detection](fraud-claims) | Helps reduce the risk of fraudulent claims while increasing claim efficienecy processing.
| Host: https://app.datarobot.com | Host: https://app.eu.datarobot.com |
|---------------------------------|------------------------------------|
|  100.26.66.209  | 18.200.151.211 |
|  54.204.171.181 | 18.200.151.56  |
|  54.145.89.18   | 18.200.151.43  |
|  54.147.212.247 | 54.78.199.18   |
|  18.235.157.68  | 54.78.189.139  |
|  3.211.11.187   | 54.78.199.173  |
|  3.214.131.132  |  |
|  3.89.169.252   |  |

!!! note
    These IP addresses are reserved for DataRobot use only.
---
title: Export charts and data
description: DataRobot exports charts as PNG images and data as CSV files; use the export function to download this data.
---


# Export charts and data {: #export-charts-and-data }

Many of the DataRobot charts have images and data available for download. (You can also download all charts and data for a particular model using the Leaderboard [**Downloads**](download) tab.) DataRobot exports charts as PNG images and data as CSV files. For charts that provide both, you can export images and data to a single ZIP file.

Note that the PNG image DataRobot exports is generated from the chart as it appears in the application at the time of export. That is, it reflects any interaction you had with the chart. The table in the exported CSV file contains the data that was used to generate the chart you see at time of export.

### Export to a file {: #export-to-a-file }

To export chart data to a file, follow these steps:

1. Find the chart you want to export on the Leaderboard’s expanded model view or in **Insights** and click **Export**.

2. In the **Export** dialog box, select the content to export (not all options are available for all screens):

	- Click **.csv** to save the data as a CSV file. Optionally, you can copy [all or part](#copy-csv-data) of the results:

		![](images/export-csv.png)


	- Click **.png** to save the image(s) as PNG, with an option to include the title:

		![](images/export-png.png)

	- Click **.zip** to save both images and data in a ZIP archive.


3. In some charts, such as the ROC Curve, you can select a single chart to export. Use the **Download** option to export all charts in the insight.

	![](images/roc-multiple-charts.png)

4. Enter a file name or accept the default file name and click **Download**.

## Copy to the clipboard {: #copy-to-the-clipboard }

In addition to saving charts as PNG or CSV files, you can copy the chart image or data to the clipboard to paste into a document. Note that, in the instructions below, the text in the pop-up menus for copying may differ, depending on your browser.

### Copy charts {: #copy-charts }

To copy a chart to the clipboard, follow these steps:

1.  Click **Export** on the chart.

2.  Select **.png** to display the chart in the dialog.

3.  Right-click on the image, and select **Copy Image** from the pop-up menu.

4. Click **Close** to close the dialog, then paste the image to the desired location.

### Copy CSV data {: #copy-csv-data }

To copy the CSV data of a chart to the clipboard, follow these steps:

1.  Click **Export** on the chart.

2.  Select **.csv** to display the chart in the dialog.

3.  Highlight some or all of the text, right-click on it, and select **Copy** from the pop-up menu.

4. Click **Cancel** to close the dialog, then paste the file contents to the desired location.
??? info "Category Cloud availability"
    The **Category Cloud** insight is available on the **Models > Insights** tab and on the **Data** tab. On the **Insights** page, you can compare word clouds for a project's categorically-based models. From the **Data** page you can more easily compare clouds across features. Note that the **Category Cloud** is not created when using a multiclass target.

Keys are displayed in a color spectrum from blue to red, with blue indicating a negative effect and red indicating a positive effect. Keys that appear more frequently are displayed in a larger font size, and those that appear less frequently are displayed in smaller font sizes. 

Check the **Filter stop words** box to remove stopwords (commonly used terms that can be excluded from searches) from the display. Removing these words can improve interpretability if the words are not informative to the Auto-Tuned Summarized Categorical Model.

Mouse over a key to display the coefficient value specific to that key and to read its full name (displayed with the information to the left of the cloud). Note that the names of keys are truncated to 20 characters when displayed in the cloud and limited to 100 characters otherwise.
---
title: Public preview features
description: Read preliminary documentation for data, modeling, time series, and MLOps features currently in the DataRobot public preview pipeline.
---

# Public preview features {: #public-preview-features }

{% include 'includes/pub-preview-notice-include.md' %}

## Available public preview documentation {: #available-public-preview-documentation }

Public preview documentation is divided into five sections:

Topic | Describes...
----- | ------
[Data](data-preview/index) | Public preview features that ingest, transform, and store your data.
[AutoML ](automl-preview/index) | Public preview features that build and analyze models.
[Time series (AutoTS)](autots-preview/index) | Public preview features that support DataRobot's time-aware functionality to forecast future values.
[MLOps](mlops-preview/index) | Public preview features that deploy, monitor, manage, and govern models in production environments.
[Administrator ](admin-preview/index) | Public preview features that support platform and user account management.
## Deep dive: Imbalanced targets {: #deep-dive-imbalanced-targets }

In AML and Transaction Monitoring, the SAR rate is usually very low (1%–5%, depending on the detection scenarios); sometimes it could be even lower than 1% in extremely unproductive scenarios. In machine learning, such a problem is called _class imbalance_. The question becomes, how can you mitigate the risk of class imbalance and let the machine learn as much as possible from the limited known-suspicious activities?

DataRobot offers different techniques to handle class imbalance problems. Some techniques:

* Evaluate the model with <a target="_blank" rel="noopener noreferrer" href="https://docs.datarobot.com/en/docs/modeling/reference/model-detail/opt-metric.html#optimization-metrics"><b>different metrics</b></a>. For binary classification (the false positive reduction model here, for example), LogLoss is used as the default metric to rank models on the Leaderboard. Since the rule-based system is often unproductive, which leads to a very low SAR rate, it’s reasonable to take a look at a different metric, such as the SAR rate in the top 5% of alerts in the prioritization list. The objective of the model is to assign a higher prioritization score with a high risk alert, so it’s ideal to have a higher rate of SAR in the top tier of the prioritization score. In the example shown in the image below, the SAR rate in the top 5% of prioritization score is more than 70% (the original SAR rate is less than 10%), which indicates that the model is very effective in ranking the alert based on the SAR risk.

* DataRobot also provides flexibility for modelers when tuning hyperparameters which could also help with the class imbalance problem. In the example below, the Random Forest Classifier is tuned by enabling the balance_boostrap (a random sample with an equal amount of SAR and non-SAR alerts in each decision tree in the forest); you can see the validation score of the new ‘Balanced Random Forest Classifier’ model is slightly better than the parent model.

![](images/aml-change-metric.png)

* You can also use <a target="_blank" rel="noopener noreferrer" href="https://docs.datarobot.com/en/docs/modeling/build-models/adv-opt/smart-ds.html#smart-downsampling"><b>Smart Downsampling</b></a> (from the Advanced Options tab) to intentionally downsample the majority class (i.e., non-SAR alerts) in order to build faster models with similar accuracy.
# Version 8.0.11 {: #version-8011 }

_January 23, 2023_

The DataRobot v8.0.11 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v8.0.0 release notes for:

* [Features introduced in v8.0.0](v8.0.0-aml)

In addition to the issues listed below, DataRobot addressed several outstanding security issues. This release/update is part of DataRobot's continual effort to secure the product and platform.

## Issues fixed in v8.0.11 {: #issues-fixed-in-v8011 }

The following issues have been fixed since release v8.0.10:

## Platform {: #platform }

- PLT-8842: Makes LinkedIn and Kaggle profile fields optional on the User Profile page.

## Time series {: #time-series }

- TIME-10673: Ensures that the contained floating point value is always used when scoring time series models.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: DataRobot REST API
description: The DataRobot REST API provides a programmatic alternative to the web interface for creating and managing DataRobot projects. Use the DataRobot API to build highly accurate predictive models and deploy them into production environments.

---

# DataRobot REST API {: #datarobot-public-api }

The DataRobot REST API provides a programmatic alternative to the web interface for creating and managing DataRobot projects. Use the DataRobot API to build highly accurate predictive models and deploy them into production environments.

For information about specific endpoints, select one from the table of contents on the left.
---
title: End-to-end workflow with SAP Hana
description: Learn how to programmatically build a model with DataRobot using SAP Hana as the data source.

---

# End-to-end workflow with SAP Hana {: #end-to-end-workflow-with-sap-hana }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/blob/main/end-to-end/SAP_End_to_End/SAP_End_to_End.ipynb){ .md-button }

The scope of this accelerator provides instructions on how to use DataRobot's Python client to build a workflow that will use an existing SAP Hana JDBC driver and:

* Create credentials
* Create the training data source
* Create the predictions data source
* Create a dataset used to train the models
* Create a dataset used to make predictions
* Create a project
* Create a deployment
* Make batch and real-time predictions
* Show the total predictions made so far

There is also a playbook at the end of this notebook that describes how to create the back end SAP Hana database that will provide the data required.
# Version 7.2.7 {: #version-727 }

_December 21, 2021_

The DataRobot v7.2.7 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.2.0 release notes for:

* [Features introduced in v7.2.0](v7.2.0-aml)

## Issues fixed in v7.2.7 {: #issues-fixed-in-v727 }

The following issues have been fixed since Enterprise release v7.2.6:

### Enterprise {: #enterprise }

* EP-2133: Updates the Scoring Code build plugins to avoid the inclusion of vulnerable log4j versions.

### Time Series {: #time-series }

* TIME-9790: Fixes downsampled training predictions for Forecast Distance split models in non-downsampled Time Series projects.

### Custom Models {: #custom-models }

* RAPTOR-7296: Fixes an issue that caused transformed data to be sent to custom models during prediction explanation initialization.

### MLOps {: #mlops }

* MMM-8649: Fixes issue with deployment reports failing for clusters running with `PYTHON3_SERVICES` enabled.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Version 7.3.x
description: DataRobot Release 7.3 release announcements index page.
---

# Version 7.3.x {: #version-73x }

Following are the AutoML, time series, and MLOps announcements for the features that make up DataRobot's 7.3.x releases, first released _December 13, 2021_.

* [v7.3.x AutoML](v7.3.0-aml)
* [v7.3.x time series](v7.3.0-ats)
* [v7.3.x MLOps](v7.3-mlops)
* [Maintenance release notes](7.3-maintenance/index) for DataRobot v7.3.x.
---
title: Python code examples
description: Review comprehensive workflows, notebooks, and tutorials that help you find complete examples of common data science and machine learning workflows.

---

# Python code examples {: #python-code-examples }

The API user guide includes overviews and workflows for DataRobot's Python client that outline complete examples of common data science and machine learning workflows. Be sure to review the [API quickstart guide](api-quickstart/index) before using the notebooks below.

Topic | Describes... |
----- | ------ |
[Feature selection notebooks](feat-select/index) | Notebooks that outline Feature Importance Rank Ensembling (FIRE) and advanced feature selection with Python. |
[Build a model factory](Build-a-Model-Factory.ipynb) | A system or a set of procedures that automatically generate predictive models with little to no human intervention. |
[Make Visual AI predictions via the API](vai-pred) | Scripting code for making batch predictions for a Visual AI model via the API. |
[Using the Batch Prediction API](batch-pred-api.ipynb) | DataRobot's batch prediction API to score large datasets with a deployed DataRobot model. |
[Configure datetime partitioning](datetime-v3.ipynb) | How to use datetime partitioning to guard a project against time-based target leakage. |
[Create and schedule JDBC prediction jobs](jdbc-nb.ipynb) |	How to use DataRobot's Python client to schedule prediction jobs and write them to a JDBC database. |
[Migrate models](migrate-nb.ipynb) |	How to transfer models from one DataRobot cluster to another as an .mlpkg file. |
[Create and deploy a custom model](custom-models.ipynb) | An end-to-end workflow for creating a custom model and deploying it to make predictions. |
[Make batch predictions with Azure Blob storage](azure-pred) | How to generate SHAP-based Prediction Explanations with a use case that determines what drives home value in Iowa. |
[Make batch predictions with Google Cloud Storage](gcs-pred) | How to read input data from and write predictions back to Google Cloud Storage. |
The **Clustering** tab sets the number of clusters that DataRobot will find during Autopilot. The default number of clusters is based on number of series in the dataset.

To set the number, add or remove values from the entry box and select the value from the dropdown:

![](images/cluster-adv-opt-1.png)
 
 Note that when using Manual mode, you are prompted to set the number of clusters when building models from the Repository.
---
title: Visual AI reference
description: Do a deep dive on DataRobot's Visual AI.

---

# Visual AI reference {: #visual-ai-reference }

These sections describe the workflow and reference materials for including images as part of your DataRobot project.

Topic | Describes...
----- | ------
[Visual AI reference](vai-ref) | Learn about technological components of Visual AI.
[Visual AI tuning walkthrough](vai-tuning-guide) | See an example of the [tuning section](vai-tuning) at work. 

See considerations for working with [Visual AI](vai-model#feature-considerations).
---
title: Add custom model versions
description: Update a model's contents to create a new version of the model due to new package versions, different preprocessing steps, hyperparameters, etc.

---

# Add custom model versions {: #add-custom-model-versions }

If you want to update a model due to new package versions, different preprocessing steps, hyperparameters, and more, you can update the file contents to create a new version of the model. To upload a new version of a custom model _environment_, see [Add an environment version](custom-environments#add-an-environment-version). 

## Create a new minor version

When you update the contents of a model, the minor version (1.1, 1.2, etc.) of the model automatically updates. To create a minor custom model version, select the model from the Custom Model Workshop and navigate to the **Assemble** tab. Under the **Model** header, click **Add files** and upload the files or folders you updated. The minor version is also updated if you delete a file.

![](images/cus-model-minor-version.png)

## Create a new major version

To create a new major version of a model (1.0, 2.0, etc.): 

1. Select the model from the Custom Model Workshop and navigate to the **Assemble** tab. 

2. Under the **Model** header, click **+ New Version**.

    ![](images/cus-model-major-version.png)

3. In the **Create new model version** dialog box, select a version creation strategy and configure the new version:

    ![](images/cmodel-17.png)

    | Setting                               | Description               |
    |---------------------------------------|---------------------------|
    | **Copy contents of previous version** | Add the contents of the current version to the new version of the custom model. |
    | **Create empty version**              | Discard the contents of the current version and add new files for the new version of the custom model. |
    | **Base Environment**                  | Select the base environment of the new version. The environment of the current version is selected by default. |
    | **New version description**           | Enter a description of the new version. The version description is optional.
    | **Keep training data from previous version** |  Enable or disable adding the training data from the current version to the new custom model version. This setting is enabled by default. |

    !!! note
        The **Keep training data from previous version** option is only available if your custom model assigns training data at the version level, not the model level. For more information, see [Add training data to a custom model](custom-model-training-data).

4. Click **Create new version**.

    You can now use a new version of the model in addition to its previous versions. Select the iteration of the model that you want to use from the **Version** dropdown.

    ![](images/cmodel-11.png)
To create a deployment from the Leaderboard:

1. From the Leaderboard, select the model to use for generating predictions and click **Predict > Deploy**. The **Deploy model** page lets you create a new deployment for the selected model. In this example, the model is both recommended for deployment and prepared for deployment:

    ![](images/deploy-model-leaderboard-prepared.png)

    !!! note
        The **Deploy** tab behaves differently in environments without a dedicated prediction server, as described in the section on [shared modeling workers](#use-shared-modeling-workers), below.

2. If the model is not [prepared for deployment](model-rec-process#prepare-a-model-for-deployment) as in the example below, best practice recommends that you click **Prepare for deployment**. DataRobot runs feature impact, retrains the model on a reduced feature list, trains on a higher sample size and then the full sample size (latest data for date/time partitioned projects).

    ![](images/deploy-model-leaderboard-unprepared.png)

3. If using a binary classification model, set the [**Prediction threshold**](threshold) before proceeding. (New models added to the Leaderboard are assigned the default value 0.5.)

    !!! note
        If you set the prediction threshold before the [deployment preparation process](model-rec-process), the value does not persist through the process.  When deploying the prepared model, if you want it to use a value other than the default, set the value after the model has the **Prepared for Deployment** badge applied.

4. To deploy a prepared model, click **Deploy model**.

5. Add [deployment information and create the deployment](add-deploy-info).
---
title: Word Cloud repeats
description: Why would a term would show up multiple times in a word cloud?
---

# Word Cloud repeats {: #word-cloud-repeats }

<span style="color:red;font-size: 1rem"> `Robot 1`</span>

**Why would a term would show up multiple times in a word cloud? **

And for those occurrences, why would they have different coefficients?

<span style="color:red;font-size: 1rem"> `Robot 2`</span>

Is the word cloud combining multiple text columns?

<span style="color:red;font-size: 1rem"> `Robot 1`</span>

Ah, yes, that is definitely it, thank you!! Is there a way to see a word cloud on only one feature?

<span style="color:red;font-size: 1rem"> `Robot 2`</span>

The simplest solution would be to use a feature list and train a model on just the one text feature you’re interested in.


<span style="color:red;font-size: 1rem"> `Robot 1`</span>

^^ That’s exactly what I ended up doing. Thank you so much for the quick answer.